Next Generation Radio Unit

Abstract

This research introduces an innovative approach to optimize channel filtering and Error Vector Magnitude (EVM) measurement in modern telecommunications radio units through the integration of artificial intelligence and machine learning techniques. The proposed framework addresses the limitations of traditional fixed-coefficient filtering and conventional EVM measurement methods by implementing an adaptive, real-time system that enhances performance while maintaining computational efficiency. Our solution combines deep learning models with traditional digital signal processing techniques, creating a hybrid architecture that demonstrates significant improvements in filtering efficiency and EVM measurement accuracy. The system shows robust performance across various channel conditions and modulation schemes, particularly in high-interference environments. This implementation successfully addresses the challenges of real-time processing constraints and resource optimization, providing a scalable solution for next-generation telecommunications systems. The research contributes to advancing radio unit signal processing technology, offering practical solutions for improving telecommunications system performance and reliability. Keywords: Adaptive Filtering, EVM Measurements, Deep learning.